Abstract
As the computer technology becomes more and more convenient, people also pay more attention to security of life. Study of surveillance system becomes an important issue nowadays, where an accurate object tracking is one of key topics. When tracking objects, there are two main steps. First, object detection, which can locate the foreground objects of interest from the video, is needed. Second, we have to propose a good system that can track objects with several kinds of interaction. For object detection, Kim et al. proposed a Codebook background modeling algorithm which can detect objects in a varying background. However, sometimes ghost objects are detected. For object tracking, there are usually complicated interactions of multiple objects, causing difficulty. In this thesis, we utilize and make some modifications on Codebook model so that it can provide a more complete result for detecting objects. Then an edge-based model is combined with Codebook model that can reduce some ghost objects during object detection. We classify all possible events into five different situations, and each situation is processed by a specific algorithm. A robust tracking result is obtained from the object tracking system. In the experiment, by the combination of Codebook model and edge-base model, it can effectively reduce occurrence of ghost objects. Our proposed tracking algorithm also solves some possible difficulties. When dealing with splitting, objects can be distinguished into correct targets. By using a proposed edge-based meanshift algorithm, objects, which are very small, can be kept tracking successfully. Our system also produces precise result for low frame rate video. The proposed system works at 10 - 15 fps and achieves real-time operation.